RL seeks to learn a policy that maximizes the expected discounted future rewards for MDP [1]}.
This contrasts to supervised learning, which seeks to learn a function to map data to labels.
When presented with a dataset of states and actions from a given policy in an environment, a model can be trained using supervised learning to map the states to action in a way that mimics the dataset, which is called imitation learning [2]}, [3]}, [4]}.
